Integrate IBM Watson with your application for better analytics.

Watson consists of Cognitive computing and big data analytics. Cognitive computing is different from mathematical computing. Watson learns unstructured data which is 80% data, literature, blogs, tweets etc. It reads, interprets and understands contexts like a human mind. It finds the real intent of the user. It learns languages, jargons and even the way to think. Watson can guide professionals to improve its expertise, what is required is to create a corpus with the relevant data, curates the content and ingestion is performed. The corpus consists of training data set which will be loaded by the user based on a question and answer format. New insights and complex problems will be solved by Watson after building the corpus.

How to integrate IBM Watson with any application?

Watson can be integrated easily with any application using iframe or API (QAAPI). Natural language or machine learning concepts are not required for the external application or user. JSON is accepted as accept type and content type by QAAPI. Watson can be embedded inside the application and all the features of cognitive computing can be utilized. The user authentication would be user id and password created during registration. For every request sent to the Watson a response will be provided with a Authentication token through SSL for security which is valid for 10 min currently. The questions posed to Watson can be synchronous or Asynchronous. In Asynchronous state, questions are posed in bulk and the responses are pooled which are timed based on the user input. This time is the processing time which Watson can take to respond. The way the API (QAAPI)interacts with Watson is using REST services. The response comprises of an answer, metadata, confidence score and evidence which will help the user to understand the probability with which the answer is relevant to the question posed.

Watson is an answer to many issues faced by the industry, in order to improve margins, businesses switched their focus on optimizing marketing, operating, human resource and sales expense based on analytical inputs. With analytics at nascent stage, data cleansing, data integration were laborious task for the analytics team. This used to consume enormous time as well and the skill required was not commonly available as someone doing this job had to be mathematically sound. The quality of analysed data and the business decisions depended on the efficiency of this professional. As always, there was possibility of human error creeping in the analysis which might lead to wrong decisions.